The reality is you can find “evidence” for almost any narrative. Limit the sample size, cherry-pick studies, etc. Systematic reviews, meta analyses, and randomized controlled trials are all susceptible to selective interpretation/narrative fallacy.
@nntaleb 1/10
Science is assumed to be “evidence-based” but that term alone doesn’t mean much. What constitutes good evidence? How is evidence being used? Is it supporting or refuting a hypothesis? Was the hypothesis and experimental design predetermined or found ex post facto?
The reality is you can find “evidence” for almost any narrative. Limit the sample size, cherry-pick studies, etc. Systematic reviews, meta analyses, and randomized controlled trials are all susceptible to selective interpretation/narrative fallacy.
At the heart of the problem is the over-reliance on simplistic statistical techniques that do little more than quantify 2 things moving together.
Take Pearson’s correlation, based on covariance. Variation can increase simultaneously across 2 variables for countless reasons, most of which are spurious. Yet this simple notion of “causality” undergirds much of scientific literature.
Information-theoretic (entropy based) approaches on the other hand can assess *general* measures of dependence. Rather than some specialized (linear) view based on concurrent variation, entropy encompasses the amount of information contained in and between variables.
If you were genuinely interested in giving the term “evidence” an authentic and reliable meaning then the methods used to underpin an assertion would be rigorous.
We wouldn’t look to conveniently simplistic methods to denote something as evidential, rather we would look for a measure capable of assessing the expected amount of information held in a random variable; there is nothing more fundamental than information.
Consider Mutual Information (MI), which quantifies the amount of information obtained about one random variable through observing another random variable. This observing of the relationship between variables is what measurement and evidence is all about.
MI determines how different joint entropy is from marginal entropies. If there is a genuine dependence between variables we would expect information gathered from all variables at once (joint) to be less than the sum of information from independent variables (marginals).
If “evidence-based” science was genuinely invested in authentic measurement it would leverage *general* measures of dependence; that demands an approach rooted in information-theory. Without entropy you’re just picking data, choosing a narrative, and calling it “evidence.”
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JUST ONE PERSON—UK 🇬🇧 scientists think one immunocompromised person who cleared virus slowly & only partially wiped out an infection, leaving behind genetically-hardier viruses that rebound & learn how to survive better. That’s likely how #B117 started. 🧵 https://t.co/bMMjM8Hiuz
2) The leading hypothesis is that the new variant evolved within just one person, chronically infected with the virus for so long it was able to evolve into a new, more infectious form.
same thing happened in Boston in another immunocompromised person that was sick for 155 days.
3) What happened in Boston with one 45 year old man who was highly infectious for 155 days straight before he died... is exactly what scientists think happened in Kent, England that gave rise to #B117.
4) Doctors were shocked to find virus has evolved many different forms inside of this one immunocompromised man. 20 new mutations in one virus, akin to the #B117. This is possibly how #B1351 in South Africa 🇿🇦 and #P1 in Brazil 🇧🇷 also evolved.
5) “On its own, the appearance of a new variant in genomic databases doesn’t tell us much. “That’s just one genome amongst thousands every week. It wouldn’t necessarily stick out,” says Oliver Pybus, a professor of evolution and infectious disease at Oxford.
2) The leading hypothesis is that the new variant evolved within just one person, chronically infected with the virus for so long it was able to evolve into a new, more infectious form.
same thing happened in Boston in another immunocompromised person that was sick for 155 days.
3) What happened in Boston with one 45 year old man who was highly infectious for 155 days straight before he died... is exactly what scientists think happened in Kent, England that gave rise to #B117.
Immunocompromised 45 year old suffered from #COVID19 for 155 days before he died. The virus was changing very quickly inside the man's body\u2014it acquired a big cluster of >20 mutations\u2014resembled the same ones seen in #B117 & #B1351. (NPR audio Part 1 of 2)\U0001f9f5https://t.co/7kWiBZ1xGk pic.twitter.com/ZJ7AExB78Y
— Eric Feigl-Ding (@DrEricDing) February 8, 2021
4) Doctors were shocked to find virus has evolved many different forms inside of this one immunocompromised man. 20 new mutations in one virus, akin to the #B117. This is possibly how #B1351 in South Africa 🇿🇦 and #P1 in Brazil 🇧🇷 also evolved.
2) NPR report audio part 2 of 2:
— Eric Feigl-Ding (@DrEricDing) February 8, 2021
Dr. Li couldn't believe what they found. "I was shocked," he says. "When I saw the virus sequences, I knew that we were dealing with something completely different and potentially very important." pic.twitter.com/HT3Yt6djFd
5) “On its own, the appearance of a new variant in genomic databases doesn’t tell us much. “That’s just one genome amongst thousands every week. It wouldn’t necessarily stick out,” says Oliver Pybus, a professor of evolution and infectious disease at Oxford.
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